AI Driven OBOS Analyzer (Zeiierman)

٢٧ يوليو ٢٠٢٠

█ Overview
AI Driven OBOS Analyzer (Zeiierman) reframes price into an adaptive Overbought/Oversold (OBOS) regime map. Rather than relying on a single oscillator threshold, it uses a responsive price function and an instance-based learner that classifies the current state by comparing it to its most similar historical states. The result is a forward-useful view of where participation is likely imbalanced (buyers dominating vs. sellers dominating), rendered as colored candles, regime boxes, and automatically drawn equilibrium lines.

⚪ Why This One Is Unique
This system stands out because its pricing engine adapts to market behavior rather than relying on a fixed formula. Rather than committing to a single filtering function or reaction speed, it reshapes its internal price view in real time, creating an OBOS framework that moves with the market’s rhythm and offers a more natural sense of when pressure is building on either side.

Its regime detection is equally distinct. Instead of static thresholds, it relies on similarity-based evaluation, comparing the current state to historically comparable periods and letting those past states vote on whether the market currently sits in a bull- or bear-leaning regime. Separate controls for how many comparisons matter and how large the reference cohort should be allow you to adjust for responsiveness or stability. As dominance phases emerge, structural regions build and then lock, creating a clear visual map of where participation meaningfully shifted between buyers and sellers.

█ Main feature

⚪ Overbought/Oversold Layer
The OBOS layer highlights when the market enters a buyer-dominant or seller-dominant phase and preserves those phases as structural reference levels. When the learner identifies a bull-dominant state, candles and a green regime box appear from the start of that dominance; once the regime concludes, the tool places an equilibrium line, a forward-projected level representing the regime’s internal balance point.

Bear-dominant phases follow the same logic with red boxes and bearish equilibrium lines. These equilibrium zones act as the anchor for the entire overbought/oversold structure, functioning as balanced points where market pressure previously shifted. A price above equilibrium often favors a bullish bias, while a price below equilibrium tends to favor a bearish bias. Traders can watch how the price behaves when revisiting these lines, such as retests, holds, reclaims, or failures, to gauge whether previous dominance levels are being respected, rejected, or flipped, turning past regime behavior into meaningful, trade-relevant context.

█ How to Use

⚪ Overbought/Oversold Trading
Overbought and oversold trading is one of the most recognized setups in technical analysis. It signals when the market has moved too far or too fast in one direction, creating an overextended move and a clear imbalance between buyers and sellers. These imbalances tend to “rebalance” through pullbacks or reversals as price fills the displaced area. Because of this, overbought and oversold zones become natural regions where traders look for turning points or counter-moves. These areas are also great spots to secure partial profits if you’re already in a position.

Reversal trading
Reversal trading based on overbought and oversold conditions can work extremely well in ranging markets. But you still need proper market context before going contrarian. Don’t rely on overbought or oversold signals in isolation.

Profit-taking
Profit-taking is about locking in gains as the market moves in your favor. Overbought and oversold zones create natural spots to secure partial profits, and when these zones end, that shift is a great moment to take some profit off the table.

⚪Buying and Selling Pressure Trading
When overbought or oversold conditions appear, they reflect a strong dominance in buying or selling pressure. Overbought means buyers are in control; oversold means sellers are in control. These conditions can extend for some time, and the price can continue moving in that direction until buying and selling pressure finally equalize again.

Buying-Pressure
When the market enters an overbought zone, traders can look for entries aligned with that pressure to ride the momentum until it fades. A common approach is to identify an overbought imbalance on a higher timeframe, such as the 1-hour chart, and then switch to a lower timeframe, such as the 1-minute chart, to locate oversold pockets. These lower-timeframe oversold areas offer attractive long entries, assuming the higher-timeframe buying pressure continues to drive prices.

Selling-Pressure
Selling-pressure trading works the same way but in reverse. When the market enters an oversold zone, sellers dominate. Traders can use a higher-timeframe oversold imbalance as the directional bias and then look at lower timeframes for small overbought zones to enter short. These micro overbought areas become efficient entry points to ride the broader selling pressure until it resolves.

⚪ Equilibrium Trading
Overbought and oversold zones generate an equilibrium line once the zone completes. This line represents the core shift in buying or selling pressure within that regime. When price revisits an equilibrium line, retests and reversals are common. If the price holds above an equilibrium line, traders can lean toward a bullish bias; if it holds below, a bearish bias becomes more likely. These equilibrium levels act as clean, reliable reference points for directional confirmation and timing.


█ How It Works

⚪ Responsive Price Function
Price is reframed through an adaptive transformation that behaves like a dynamic response surface, adjusting its sensitivity to volatility, curvature, and micro-structure noise. Instead of a fixed smoothing rule, the engine applies an elastic filtering function that adapts in real time, preserving meaningful structure while reducing transient distortions. The outcome is a stable yet agile price backbone that drives all regime evaluation.

• Calculation: Employs a parameterized smoothing functional that adjusts its horizon dynamically, reducing distortion around turning points and keeping the model’s internal state closely aligned with actual price movement.
  

⚪ Instance-Based Regime Classifier
Each bar is embedded into a feature space defined by its behavior relative to the model’s adaptive price state. The system then performs a similarity search across a broad historical cohort, identifying the closest structural analogs and allowing them to vote on the current bar’s regime identity. This instance-driven process avoids rigid thresholds and instead adapts fluidly to the market’s prevailing volatility conditions and structural rhythm.

• Calculation: Executes an enhanced weighted nearest-neighbor inference process where similarity scores shape probabilistic voting, concentrating influence on the most contextually relevant examples to yield a stable bull or bear regime classification.
  

⚪ Regime Boxes & Exit Equilibrium Lines
Active regimes accumulate their structural boundaries as the market evolves, forming a real-time “regime envelope” that expresses the spatial footprint of buyer or seller dominance. When the regime ends, the segment is sealed, and an equilibrium line is projected from its internal centroid. This equilibrium expresses the pressure balance point of the regime and acts as a durable reference level for future reactions, reclaims, or breaks.

• Calculation: Utilizes event-based segmentation with stateful envelope aggregation and centroid extraction, converting each completed regime into a persistent equilibrium marker that carries forward as a reactive structural level.
  

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Disclaimer

The content provided in my scripts, indicators, ideas, algorithms, and systems is for educational and informational purposes only. It does not constitute financial advice, investment recommendations, or a solicitation to buy or sell any financial instruments. I will not accept liability for any loss or damage, including without limitation any loss of profit, which may arise directly or indirectly from the use of or reliance on such information.

All investments involve risk, and the past performance of a security, industry, sector, market, financial product, trading strategy, backtest, or individual's trading does not guarantee future results or returns. Investors are fully responsible for any investment decisions they make. Such decisions should be based solely on an evaluation of their financial circumstances, investment objectives, risk tolerance, and liquidity needs.

٢١ سبتمبر ٢٠٢٠

ملاحظات الأخبار

Major improvements!

• Improved calculations for better accuracy.
  

Best

٢٤ سبتمبر ٢٠٢٠

ملاحظات الأخبار

Minor improvements!

Best

٢٣ سبتمبر ٢٠٢١

ملاحظات الأخبار

Improvements!

• Performance improvements
  
• Tooltips added
  
• Alerts added
  

٢٥ ديسمبر ٢٠٢٣

ملاحظات الأخبار

Major Improvements

• AI driven OBOS Analyser added